In recent years, the world has witnessed explosive growth in the demand for wireless communications and it is predicted that this demand will increase in the future. There are already over 500 million users that subscribe to cellular telephone services and the number is continually increasing. Eventually, in the not too distant future, the number of cellular subscribers will exceed the number of fixed line telephone installations. Already, in many cases, the revenues from mobile services already exceeds that for fixed line services even though the amount of traffic generated through mobile phones is much less than in fixed networks.
Other related wireless technologies have experienced growth similar to that of cellular. For example, cordless telephony, two way radio trunking systems, paging (one way and two way), messaging, wireless local area networks (WLANs) and wireless local loops (WLLs). In addition, new broadband communication schemes are rapidly being deployed to provide users with increased bandwidth and faster access to the Internet. Broadband services such as xDSL, short range high speed wireless connections, high rate satellite downlink (and the uplink in some cases) are being offered to users in more and more locations.
In connection with cellular services, the majority of users currently subscribe to digital cellular networks. Almost all new cellular handsets sold to customers are based on digital technology, typically second generation digital technology. Currently, third generation digital networks are being designed and tested which will be able to support data packet networks and much higher data rates. The first generation analog systems comprise the well known protocols AMPS, TACS, etc. The digital systems comprise GSM, GERAN, TDMA (IS-136) or CDMA (IS-95), for example.
A diagram illustrating an example prior art communication system employing an inner and outer encoder in the transmitter, inner and outer decoding stages in the receiver and a noise source after the channel is shown in FIG. 1. The communication system, generally referenced 10, represents the typical scheme that may be used in many of the communication services described above. In such a system, the transmitter 12 comprises an encoder 14, symbol generator 16 and modulator 18. Input data bits to be transmitted are input to the encoder 14, which may comprise an error correction encoder such as a Reed Solomon encoder, a convolutional encoder, a parity bit generator, etc. The encoder functions to add redundancy bits to enable errors in transmission to be located and fixed.
It is noted that both the inner and outer decoders in the receiver have complementary encoders in the transmitter. The outer encoder in the transmitter comprises the encoder 14, e.g., Reed Solomon, etc. The inner encoder comprises the channel 20 which often times can be modeled as an L-symbol long FIR-type channel.
The bits output of the encoder are then mapped to symbols by the symbol generator 16. The symbol generator functions to transform the bits to modulator symbols. For example, an 8-PSK modulator converts input bits into one of eight symbols. Thus, the symbol generator generates a symbol for every three input bits.
The output from the mapper is input to the modulator which receives symbols in the M-ary alphabet and generates the analog signal that is subsequently transmitted over the channel 20. The channel may comprise a mobile wireless channel, e.g., cellular, cordless, a fixed wireless channel, e.g., satellite, or may comprise a wired channel, e.g., xDSL, ISDN, Ethernet, etc. The processing performed in the transmitter is intended to generate a signal that can be transmitted over the channel so as to provide robust, error free detection by the receiver.
At the receiver 30, the analog signal from the channel is input to front end circuitry 32 which demodulates and samples the received signal to generate received samples y(k) 40. The symbols are first input to an inner decoder 34. An example of an inner decoder is an equalizer which compensates for the ISI caused by the delay and time spreading of the channel. Examples of commonly used types of equalizers include the maximum likelihood sequence estimation (MLSE) equalizer that utilize the well known Viterbi Algorithm (VA), linear equalizer and decision feedback equalizer (DFE). The equalizer attempts to the detect the symbols that were originally transmitted by the modulator.
In the typical wireless receiver, the inner decoder generates its output using an estimate of the channel. The channel estimation module 38 is operative to generate a channel estimate represented by ĥ(k) that is used by the inner decoder 34. The channel estimation is generated using the received input samples y(k) 40, the ideal training sequence and the received training sequence.
The output of the inner decoder comprises symbols s(k) 42 which represent hard decisions. The hard decisions are then input to an outer decoder 36 which functions to locate and fix errors using the redundancy inserted by the encoder. The outer decoder generates the binary receive data A(k) 44.
An example of an outer decoder is a convolutional decoder that utilizes the Viterbi Algorithm. The Viterbi Algorithm is widely used in communication systems and has been adapted to perform functions including demodulation, decoding, equalization, etc. Many systems utilize the Viterbi Algorithm in both the inner and outer decoding stages.
For example, consider a receiver adapted to receive a GSM or GERAN signal. Such a system utilizes convolutional coding for performing Forward Error Correction (FEC) over channels that require equalization. The equalizer and outer FEC decoder typically used employ the Viterbi Algorithm in their operation.
There exists a class of decoders that provide improved performance by utilizing soft information about the received symbols rather than only hard decisions. Examples include turbo decoders and soft decision convolutional forward error correction decoders utilizing the Viterbi Algorithm, etc. The advantage of a Viterbi decoder is that it can efficiently process of decision information. This class of decoders provides better performance by taking into account soft information about the reliability of the received symbol. The improved performance of the decoder cannot be realized, however, when soft information about the received symbols is not available.
The technique of equalization is a commonly used method of improving performance with channels exhibiting inter symbol interference. Equalizers such as those described above are adapted to output hard decisions. In the case where an optimal decoder is to be used, soft bit decisions rather than hard bit decisions are required (where a hard bit decision is a bit value (0 or 1) and a soft decision consists of a bit value and the decision reliability). In this case the equalizer is adapted to generate soft outputs, namely it produces soft symbol decisions.
In order to enable accurate and reliable computation of decision reliability based on the equalizer output metric, it is desirable that the soft decisions input to the soft outer decoder be normalized to the same mean noise power σ. Using non-normalized soft decisions negatively affects the overall performance of the receiver.
Note that in systems where the noise statistic does not vary with time, receiver performance may not be affected much if non-normalized soft decisions are used. In most real world communication systems, however, the noise variance varies with time and cannot be assumed constant. In such communication systems, receiver performance can be significantly improved if the variation in noise statistic is factored into the determination of the soft decisions.